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bigml 0.7.5

BigML makes machine learning easy by taking care
of the details required to add data-driven decisions and predictive
power to your company. Unlike other machine learning services, BigML
creates
beautiful predictive models that
can be easily understood and interacted with.

These BigML Python bindings allow you to interact with BigML.io, the API
for BigML. You can use it to easily create, retrieve, list, update, and
delete BigML resources (i.e., sources, datasets, models and,
predictions).

Importing the module

Authentication

All the requests to BigML.io must be authenticated using your username
and API key and are always
transmitted over HTTPS.

This module will look for your username and API key in the environment
variables BIGML_USERNAME and BIGML_API_KEY respectively. You can
add the following lines to your .bashrc or .bash_profile to set
those variables automatically when you log in:

Otherwise, you can initialize directly when instantiating the BigML
class as follows:

api = BigML('myusername', 'ae579e7e53fb9abd646a6ff8aa99d4afe83ac291')

Also, you can initialize the library to work in the Sandbox environment by
passing the parameter dev_mode:

api = BigML(dev_mode=True)

Quick Start

Imagine that you want to use this csv
file containing the Iris
flower dataset to
predict the species of a flower whose sepal length is 5 and
whose sepal width is 2.5. A preview of the dataset is shown
below. It has 4 numeric fields: sepal length, sepal width,
petal length, petal width and a categorical field: species.
By default, BigML considers the last field in the dataset as the
objective field (i.e., the field that you want to generate predictions
for).